Metadata-Version: 2.4
Name: MEDS_visualizations
Version: 0.0.1
Summary: A framework for compiling simple, mapreduce style pipelines over MEDS datasets.
Author-email: Matthew McDermott <mattmcdermott8@gmail.com>
Project-URL: Homepage, https://github.com/mmcdermott/MEDS_visualizations
Project-URL: Issues, https://github.com/mmcdermott/MEDS_visualizations/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: polars<=1.27.9,>=1.26.0
Requires-Dist: plotly
Requires-Dist: meds~=0.4.0
Requires-Dist: jupyter
Provides-Extra: dev
Requires-Dist: pre-commit<4; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Provides-Extra: tests
Requires-Dist: pytest-cov; extra == "tests"
Requires-Dist: pytest; extra == "tests"
Requires-Dist: meds_testing_helpers~=0.3.0; extra == "tests"
Dynamic: license-file

# MEDS Visualizations

Visualization tools for MEDS datasets.

> [!WARNING]
> This is a work in progress. The API and functionality are very likely change as we develop the library.

## Installation

```bash
pip install MEDS_visualizations
```

## Usage

In a Jupyter notebook, you can load whatever combination of data extractor and plotter you want:

```python
from MEDS_visualizations.extractors import CodeFrequency
from MEDS_visualizations.plotters import Bar
from MEDS_visualizations.visualization import Visualization

CF = CodeFrequency(as_proportions=True)
P = Bar(top_k=10, y_cols=["n_occurrences"])

V = Visualization(extractor=CF, plotter=P)
V.render(data_shards)
```

In the future, we anticipate

- Registering extractors and plotters via pypi entry points.
- Adding the capability to chain together arbitrary extractors and plotters to make a report in a
    visualization.
- Adding the capability to apply arbitrary filters or transformations to all data shards used to power a
    visualization.
